Data management processes for life sciences companies are consistently in flux. This era of change presents great opportunities but also significant potential pitfalls.
Life sciences companies need not only whip-smart data scientists, but also the right processes to accommodate the rapid evolution of technology and increase in the quantity of data.
- Empower internal “champions”: Successful data projects have a senior-level “champion” who commands the resources needed to ensure success. To ensure that data processes align, companies should put in place seasoned data officers who will break down silos and oversee and manage organization-wide data.
- Don’t overvalue off-the-shelf solutions: There’s an abundance of high-powered technology available that companies can implement to manage data processes. However, a company should look beyond the flashiest features and focus more on how technology will accommodate its needs. Commercial leaders should prioritize flexibility over off-the-shelf features to make the most of their technology investments and ensure that the technology can be integrated easily with other, disparate systems.
- Protect data systems: Life sciences companies must design data management systems and operations to be able to withstand a variety of issues and challenges related to incoming data. After all, errors are inevitable and data is constantly changing. So, companies must also design systems to account for new market definitions, national drug codes or business units.
Data engineers must think of the multitude of things that could go wrong or change with data and test thoroughly to ensure these issues don’t wreck the company’s data management processes and force costly system rebuilds. Accounting for these contingencies is crucial as life sciences companies process more data.
The future of data engineering
With so much more data to process and high-powered technology tools to deploy to analyze that data today, it’s time to democratize data. Life sciences organizations must empower their users to mine data for key insights that drive commercial performance improvements. Centralized data management can facilitate this by allowing more users to pull all the various strands together and uncover meaningful insights quickly.
New revolutionary advancements will soon start to supercharge data processing and analysis in the life sciences industry. As a result, data scientists will increasingly be able to utilize integrated data to generate meaningful insights for their organizations. Data scientists and commercial operations leaders in the life sciences industry need to prepare for this era of centralized data without delay.